At present, face recognition systems are more and more frequently applied to scenarios that require an ID authentication in fields like security, finance etc., such as remote bank account opening system, access control system, remote transaction operating verification system, etc. In these application fields with high security level, in addition to ensuring that a face similarity of a person to be verified matches with library data stored in a database, first of all, it needs that the person to be verified is a legitimate biological living body. That is to say, the face recognition system needs to be able to prevent an attacker from attacking using pictures, 3D face models, or masks and so on.
The method for solving the above problem is usually called liveness detection, which aims to determine whether an obtained biological feature comes from a living, in-field, real person. Mature liveness verification schemes has not existed among technology products on market yet, conventional liveness detection techniques either depend on specific hardware devices (such as infrared camera, depth camera) or can prevent only simple attacks from static pictures. In addition, most of the liveness detection systems existing in the prior art are cooperated-style, i.e., requiring a person being tested to make a corresponding action or stay still in place for a period of time according to an instruction from the systems, however it affects user's experience and efficiency of liveness detection. Besides, for example, accuracy and robustness of another method for determining whether there is an image border in a detected image can hardly meet the actual demands.